Image processing device, image processing method, program, and recording medium for detection of epidermis pattern

ABSTRACT

An epidermis pattern detection unit detects epidermis patterns in an epidermis image captured from the epidermis of skin by an epidermis image capturing unit. An acquired element analysis unit analyzes uniformity of shapes of the epidermis patterns in the epidermis image. A texture evaluation unit evaluates a texture state of the skin based on the uniformity of shapes of the epidermis patterns. The present technology, for example, may be applied to systems that evaluate the texture state of the skin.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. application Ser. No.14/718,647, filed May 21, 2015, which is a divisional of U.S.application Ser. No. 13/462,949, filed May 3, 2012, which is now U.S.Pat. No. 9,122,906 issued on Sep. 1, 2015, which claims benefit ofJapanese Patent Application No. JP-2011-115182, filed May 23, 2011, thedisclosures of which are incorporated herein by reference.

BACKGROUND

The present technology relates to an image processing device, an imageprocessing method, a program, and a recording medium, and moreparticularly, to an image processing device, an image processing method,a program, and a recording medium, which are suitable for evaluatingtexture states of skin.

Techniques for evaluating texture states of skin by analyzing a skinimage captured from the skin of a human are proposed in related art.

For example, a technique of evaluating the fineness of the texture bymeans of the number of skin ridges in the skin image, and evaluating theorientation of the texture based on spectral shapes of the imageFourier-transformed from the skin image is proposed (see Japanese PatentLaid-Open Publication No. 2006-61170).

For example, another technique of analyzing the textures or pores of theskin based on sizes of the pores in the skin image, clearness of theskin grooves, fineness of the skin ridges, and a degree of circularityof the skin ridges is proposed (see Japanese Patent Laid-OpenPublication No. 2006-305184).

SUMMARY

However, in the technique disclosed in Japanese Patent Laid-OpenPublication No. 2006-61170, the texture state of the skin may not beevaluated correctly because shapes of the textures are not taken intoconsideration. In addition, since the shapes of the skin ridges varyvastly, the texture state of the skin may not be evaluated correctlyeven when the degree of circularity of the skin ridges is taken intoconsideration in accordance with the technique disclosed in JapanesePatent Laid-Open Publication No. 2006-305184.

The present technology is made to enable the texture state of the skinto be evaluated.

According to a first aspect of the present technology, there is providedan image processing device, which includes an epidermis patterndetection unit configured to detect epidermis patterns that are patternsof an epidermis in an epidermis image captured from the epidermis ofskin, an analysis unit configured to analyze uniformity of shapes of theepidermis patterns, and an evaluation unit configured to evaluate atexture state of the skin based on the uniformity of shapes of theepidermis patterns.

The analysis unit may further analyze at least one of uniformity ofsizes of the epidermis patterns and uniformity of distributions of edgedirections of the epidermis patterns, and the evaluation unit mayfurther evaluate the texture state of the skin based on at least one ofthe uniformity of sizes of the epidermis patterns and the uniformity ofdistribution of edge directions of the epidermis patterns.

The analysis unit may further analyze a ratio at which the epidermispatterns have predetermined shapes, and the evaluation unit may furtherevaluate the texture state of the skin based on the ratio at which theepidermis patterns have the predetermined shapes.

The epidermis patterns may be patterns formed on the epidermis by skinridges or skin grooves.

According to the first aspect of the present technology, there isprovided an image processing method performed by an image processingdevice configured to evaluate a texture state of skin, which includesdetecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of the skin, analyzinguniformity of shapes of the epidermis patterns, and evaluating thetexture state of the skin based on the uniformity of shapes of theepidermis patterns.

According to the first aspect of the present technology, there isprovided a program for causing a computer to execute operationsincluding detecting epidermis patterns that are patterns of an epidermisin an epidermis image captured from the epidermis of skin, analyzinguniformity of shapes of the epidermis patterns, and evaluating a texturestate of the skin based on the uniformity of shapes of the epidermispatterns.

According to a second aspect of the present technology, there isprovided an image processing device, which includes an epidermis patterndetection unit configured to detect epidermis patterns that are patternsof an epidermis in an epidermis image captured from the epidermis ofskin, an acquired element analysis unit configured to analyze acquiredelements among elements indicating a texture state of the skin based onthe detected epidermis patterns, an inherent element analysis unitconfigured to analyze inherent elements among the elements indicatingthe texture state of the skin based on the detected epidermis patterns,and an evaluation unit configured to evaluate the texture state of theskin based on the analysis result from the acquired elements and theanalysis result from the inherent elements.

The acquired element analysis unit may analyze, as the acquiredelements, at least one of uniformity of shapes of the epidermispatterns, uniformity of sizes of the epidermis patterns, and uniformityof distribution of edge directions of the epidermis patterns, and theinherent element analysis unit may analyze, as the inherent elements,the number of the epidermis patterns per unit area.

The evaluation unit may calculate an evaluation value of the texturestate of the skin by weighting and adding an evaluation value based onthe analysis result from the acquired elements and an evaluation valuebased on the analysis result from the inherent elements.

The epidermis patterns may be patterns formed on the epidermis by skinridges or skin grooves.

According to the second aspect of the present technology, there isprovided an image processing method performed by an image processingdevice configured to evaluate a texture state of skin, which includesdetecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of the skin, analyzingacquired elements among elements indicating the texture state of theskin based on the detected epidermis patterns, analyzing inherentelements among the elements indicating the texture state of the skinbased on the detected epidermis patterns, and evaluating the texturestate of the skin based on the analysis result from the acquiredelements and the analysis result from the inherent elements.

According to the second aspect of the present technology, there isprovided a program for causing a computer to execute operations whichincludes detecting epidermis patterns that are patterns of an epidermisin an epidermis image captured from the epidermis of skin, analyzingacquired elements among elements indicating a texture state of the skinbased on the detected epidermis patterns, analyzing inherent elementsamong the elements indicating the texture state of the skin based on thedetected epidermis patterns, and evaluating the texture state of theskin based on the analysis result from the acquired elements and theanalysis result from the inherent elements.

According to a third aspect of the present technology, there is providedan image processing device, which includes an epidermis patterndetection unit configured to detect epidermis patterns that are patternsof an epidermis in an epidermis image captured from the epidermis ofskin, a dermis pattern detection unit configured to detect dermispatterns that are patterns of a dermis in a dermis image captured fromthe dermis of the skin, an acquired element analysis unit configured toanalyze acquired elements among elements indicating a texture state ofthe skin based on the detected epidermis patterns, an inherent elementanalysis unit configured to analyze inherent elements among the elementsindicating the texture state of the skin based on the detected dermispatterns, and an evaluation unit configured to evaluate the texturestate of the skin based on the analysis result from the acquiredelements and the analysis result from the inherent elements.

The acquired element analysis unit may analyze, as the acquiredelements, at least one of uniformity of shapes of the epidermispatterns, uniformity of sizes of the epidermis patterns, and uniformityof distribution of edge directions of the epidermis patterns, and theinherent element analysis unit may analyze, as the inherent elements, atleast one of uniformity of shapes of the dermis patterns, uniformity ofsizes of the dermis patterns, uniformity of distribution of edgedirections of the dermis patterns, and the number of the dermis patternsper unit area.

The inherent element analysis unit may further analyze, as the inherentelement, the number of the epidermis patterns per unit area based on thedetected epidermis patterns.

The evaluation unit may calculate an evaluation value of the texturestate of the skin by weighting and adding an evaluation value based onthe analysis result from the acquired elements and an evaluation valuebased on the analysis result from the inherent elements.

The epidermis patterns may be patterns formed on the epidermis by skinridges or skin grooves, and the dermis patterns may be patterns formedon the dermis by papillary layers.

According to the third aspect of the present technology, there isprovided an image processing method performed by an image processingdevice configured to evaluate a texture state of skin, which includesdetecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of the skin, detectingdermis patterns that are patterns of a dermis in a dermis image capturedfrom the dermis of the skin, analyzing acquired elements among elementsindicating the texture state of the skin based on the detected epidermispatterns, analyzing inherent elements among the elements indicating thetexture state of the skin based on the detected dermis patterns, andevaluating the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

According to the third aspect of the present technology, there isprovided a program for causing a computer to execute operations whichincludes detecting epidermis patterns that are patterns of an epidermisin an epidermis image captured from the epidermis of skin, detectingdermis patterns that are patterns of a dermis in a dermis image capturedfrom the dermis of the skin, analyzing acquired elements among elementsindicating a texture state of the skin based on the detected epidermispatterns, analyzing inherent elements among the elements indicating thetexture state of the skin based on the detected dermis patterns, andevaluating the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

According to the first aspect of the present technology, epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of the skin are detected, uniformity ofshapes of the epidermis patterns is analyzed, and a texture state of theskin is evaluated based on the uniformity of the shapes of the epidermispatterns.

According to the second aspect of the present technology, epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of skin are detected, acquired elementsamong elements indicating a texture state of the skin are analyzed basedon the detected epidermis patterns, inherent elements among the elementsindicating the texture state of the skin are analyzed based on thedetected epidermis patterns, and the texture state of the skin isevaluated based on the analysis result from the acquired elements andthe analysis result from the inherent elements.

According to the third aspect of the present technology, epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of the skin are detected, dermis patternsthat are patterns of a dermis in a dermis image captured from the dermisof the skin are detected, acquired elements among elements indicatingthe texture state of the skin are analyzed based on the detectedepidermis patterns, inherent elements among the elements indicating thetexture state of the skin are analyzed based on the detected dermispatterns, and the texture state of the skin is evaluated based on theanalysis result from the acquired elements and the analysis result fromthe inherent elements.

According to the first to third aspects of the present technologydescribed above, the texture state of the skin can be evaluated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an image processing system inaccordance with a first embodiment of the present technology;

FIG. 2 is a block diagram illustrating a configuration example of anepidermis image processing unit and an epidermis pattern detection unit;

FIG. 3 is a block diagram illustrating a configuration example of anacquired element analysis unit;

FIG. 4 is a block diagram illustrating a configuration example of anepidermis orientation analysis unit;

FIG. 5 is a flow chart illustrating a texture evaluation process;

FIG. 6 is a flow chart illustrating epidermis image processing;

FIG. 7 is a flow chart illustrating an epidermis pattern detectionprocess;

FIG. 8 is a diagram illustrating a specific example of a labelingprocess;

FIG. 9 is a flow chart illustrating an acquired element analysisprocess;

FIG. 10 is a diagram illustrating an example of a histogram of sizes ofskin ridge regions;

FIG. 11 is a diagram illustrating an example of a normalization curvefor calculating evaluation values of epidermis size distribution;

FIG. 12 is a flow chart illustrating epidermis shape distributionanalysis process 1;

FIG. 13 is a diagram illustrating an example of a normalization curvefor calculating evaluation values of epidermis shape distribution;

FIG. 14 is a flow chart illustrating epidermis shape distributionanalysis process 2;

FIG. 15 is a diagram illustrating examples of reference shapes;

FIG. 16 is a block diagram illustrating an image processing system inaccordance with a second embodiment of the present technology;

FIG. 17 is a block diagram illustrating a configuration example of aninherent element analysis unit;

FIG. 18 is a flow chart illustrating a texture evaluation process;

FIG. 19 is a flow chart illustrating an inherent element analysisprocess;

FIG. 20 is a diagram illustrating an example of a normalization curvefor calculating evaluation values of the number of skin ridges;

FIG. 21 is a diagram illustrating an example of a screen presentingevaluation results of the texture state of the skin;

FIG. 22 is a diagram illustrating an example of a table for obtainingcomprehensive determination values;

FIG. 23 is a diagram illustrating an example of a screen presentingevaluation results of the texture state of the skin;

FIG. 24 is a block diagram illustrating an image processing system inaccordance with a third embodiment of the present technology;

FIG. 25 is a cross-sectional diagram schematically illustrating skintissues of the skin of a human;

FIG. 26 is a diagram illustrating a configuration example of a dermisimage capturing unit;

FIG. 27 is a block diagram illustrating a configuration example of aninherent element analysis unit;

FIG. 28 is a flow chart illustrating a texture evaluation process;

FIG. 29 is a flow chart illustrating an inherent element analysisprocess; and

FIG. 30 is a block diagram illustrating a configuration example of acomputer.

DETAILED DESCRIPTION OF THE EMBODIMENT(S)

Hereinafter, preferred embodiments of the present technology will bedescribed in detail with reference to the appended drawings. Note that,in this specification and the appended drawings, structural elementsthat have substantially the same function and structure are denoted withthe same reference numerals, and repeated explanation of thesestructural elements is omitted.

Hereinafter, forms for embodying the present technology (which will bereferred to as embodiments) will be described. The description is madein the following order.

1. First embodiment (example of analyzing acquired elements only)

2. Second embodiment (example of analyzing both acquired elements andinherent elements)

3. Third embodiment (example of analyzing both epidermis and dermis)

4. Modification

<1. First Embodiment>

First, a first embodiment of the present technology will be describedwith reference to FIGS. 1 to 15.

[Configuration Example of Image Processing System 101]

FIG. 1 is a block diagram illustrating a configuration example of animage processing system 101 in accordance with the first embodiment ofthe present technology.

The image processing system 101 captures the epidermis of skin of ahuman to be evaluated (hereinafter referred to as an evaluation target),and evaluates the texture state of the skin of the evaluation targetbased on the captured image (hereinafter referred to as an epidermisimage).

The image processing system 101 includes an epidermis image capturingunit 111, an image processing unit 112, and a display unit 113.

The epidermis image capturing unit 111 captures the epidermis of theskin of the evaluation target, and supplies the captured epidermis imageto an epidermis image processing unit 121 of the image processing unit112.

In addition, the epidermis image capturing unit 111 is not limited tospecific kinds of capturing devices but may be employed for any kind ofcapturing device.

However, it is preferable that the epidermis image capturing unit 111have a manual focus adjusting function, a light source (e.g., lightemitting diode (LED)) capable of uniformly irradiating illuminationlight over an entire object, and a micro-lens capable of performingcapturing enough to recognize the surface structure of the skin.Further, it is preferable that the epidermis image capturing unit 111have an attachment to be in closer contact with the skin than the lensin order to perform every single capturing with the fixed focusposition. In this case, the attachment may have a light blockingfunction for preventing effects of external light.

In addition, it is preferable that the wavelength range of the lightsource of the epidermis image capturing unit 111 be a visible range (400to 700 nm). In a similar way, the spectral sensitivity of the imagesensor of the epidermis image capturing unit 111 may also be onecorresponding to the typical RGB where the sensitivity lies within thevisible range. However, it is preferable that the sensitivity of theshort wavelength side be higher.

The image processing unit 112 performs segmentation (regional division)on the epidermis image, and evaluates the texture of the skin of theevaluation target based on the segmentation result.

The image processing unit 112 includes an epidermis image processingunit 121, an epidermis pattern detection unit 122, an acquired elementanalysis unit 123, a texture evaluation unit 124, and an evaluationresult presentation unit 125.

The epidermis image processing unit 121, as will be described below,performs predetermined image processing such as correction or noiseremoval on the epidermis image, and supplies the image-processedepidermis image to the epidermis pattern detection unit 122 and theacquired element analysis unit 123.

The epidermis pattern detection unit 122, as will be described below,detects patterns of the epidermis within the epidermis image that areformed on the epidermis by skin ridges or skin grooves (hereinafterreferred to as epidermis patterns), and supplies the detection result(hereinafter referred to as epidermis pattern detection result) to theacquired element analysis unit 123.

The acquired element analysis unit 123, as will be described below,analyzes acquired elements among elements indicating the texture stateof the skin based on the image-processed epidermis image and theepidermis pattern detection result. The acquired element analysis unit123 supplies the analysis result to the texture evaluation unit 124.

The texture evaluation unit 124 evaluates the texture state of the skinof the evaluation target based on the analysis result from the acquiredelement analysis unit 123, and supplies the evaluation result to theevaluation result presentation unit 125.

The evaluation result presentation unit 125 causes the display unit 113to display information indicating the evaluation result of the texturestate of the skin of the evaluation target.

The display unit 113 is not limited to specific kinds of display devicesbut may be employed for any kind of display device. In addition, thedisplay unit 113 may be disposed to be exclusive for the imageprocessing system 101, or display devices of other apparatuses such astelevision receivers or mobile phones may be employed for the displayunit.

[Configuration Example of Epidermis Image Processing Unit 121 andEpidermis Pattern Detection Unit 122]

FIG. 2 is a block diagram illustrating a configuration example offunctions of the epidermis image processing unit 121 and the epidermispattern detection unit 122.

The epidermis image processing unit 121 includes an image correctionunit 141, a single channel extraction unit 142, and a noise removal unit143. In addition, the epidermis pattern detection unit 122 includes abinarization unit 151 and a labeling process unit 152.

The image correction unit 141 performs predetermined image correctionsuch as distortion correction or reduction of the epidermis image, andsupplies the corrected epidermis image to the single channel extractionunit 142.

The single channel extraction unit 142 extracts signal components of apredetermined channel from the corrected epidermis image, and suppliesthe epidermis image composed of the extracted signal components(hereinafter referred to as a single channel epidermis image) to thenoise removal unit 143.

The noise removal unit 143 removes noise of the single channel epidermisimage, and supplies the single channel epidermis image from which thenoise is removed (hereinafter referred to as a noise-removed epidermisimage) to the binarization unit 151 of the epidermis pattern detectionunit 122 and the acquired element analysis unit 123.

The binarization unit 151 performs binarization on the noise-removedepidermis image, and supplies the binarized image (hereinafter referredto as a binarized epidermis image) to the labeling process unit 152.

The labeling process unit 152 performs a labeling process on thebinarized epidermis image to detect the epidermis patterns. Inparticular, the labeling process unit 152 detects regions of the skinridges within the epidermis image (hereinafter referred to as skin ridgeregions) as the epidermis patterns. In addition, the labeling processunit 152 counts the number of skin ridge regions within the epidermisimage. And the labeling process unit 152 supplies the epidermis patterndetection result indicating the detection result of the number of skinridges and the skin ridge regions to the acquired element analysis unit123.

[Configuration Example of Acquired Element Analysis Unit 123]

FIG. 3 is a block diagram illustrating a configuration example of thefunction of the acquired element analysis unit 123. The acquired elementanalysis unit 123 includes an epidermis size distribution analysis unit171, an epidermis shape distribution analysis unit 172, an epidermisshape distribution analysis unit 173, and an epidermis orientationanalysis unit 174.

The epidermis size distribution analysis unit 171 analyzes thedistribution of sizes of the epidermis patterns. In particular, theepidermis size distribution analysis unit 171 analyzes the distributionof sizes of the skin ridge regions, and calculates the evaluation valueof the epidermis size distribution indicating the uniformity of sizes ofthe skin ridge regions. The epidermis size distribution analysis unit171 supplies the calculated evaluation value of the epidermis sizedistribution to the texture evaluation unit 124.

The epidermis shape distribution analysis unit 172 analyzes thedistribution of shapes of the epidermis patterns. In particular, theepidermis shape distribution analysis unit 172 analyzes the distributionof shapes of the skin ridge regions, and calculates the evaluation valueof the epidermis shape distribution indicating the uniformity of shapesof the skin ridge regions. The epidermis shape distribution analysisunit 172 supplies the calculated evaluation value of the epidermis shapedistribution to the texture evaluation unit 124.

The epidermis shape distribution analysis unit 173 analyzes thedistribution of shapes of the epidermis patterns from a point of viewdifferent from the epidermis shape distribution analysis unit 172. Inparticular, the epidermis shape distribution analysis unit 173 compareseach of the skin ridge regions with predetermined reference shapes, andobtains epidermis shape distribution information indicating ratios ofthe skin ridge regions having shapes close to each of the referenceshapes. The epidermis shape distribution analysis unit 173 supplies theobtained epidermis shape distribution information to the textureevaluation unit 124.

The epidermis orientation analysis unit 174 analyzes the orientation ofthe epidermis patterns. In particular, the epidermis orientationanalysis unit 174 analyzes the distribution of edge directions of theskin ridge regions, and calculates the evaluation value of the epidermisorientation indicating the uniformity of the distribution of edgedirections of the skin ridge regions. The epidermis orientation analysisunit 174 supplies the calculated evaluation value of the epidermisorientation to the texture evaluation unit 124.

In addition, the sizes, the shapes, and the edge directions of the skinridges vary in an acquired manner depending on aging, health, skin careand so forth. Accordingly, the evaluation value of the epidermis sizedistribution, the evaluation value of the epidermis shape distribution,the epidermis shape distribution information, and the evaluation valueof the epidermis orientation become indexes for evaluating the acquiredproperty of the texture state of the skin.

[Configuration Example of Epidermis Orientation Analysis Unit 174]

FIG. 4 is a block diagram illustrating the configuration example of theepidermis orientation analysis unit 174.

The epidermis orientation analysis unit 174 includes a four-directionalfiltering unit 181, an absolute value sum calculation unit 182, anaddition unit 183, a division unit 184, and an evaluation valuecalculation unit 185.

The four-directional filtering unit 181 performs edge filtering on thenoise-removed epidermis image with respect to four directions such as0°, 45°, 90°, and 135°. And the four-directional filtering unit 181supplies the obtained four filter output images to the absolute valuesum calculation unit 182.

The absolute value sum calculation unit 182 calculates the sum ofabsolute values of pixels within the image in each of the four filteroutput images. The absolute value sum calculation unit 182 then suppliesthe sum of the absolute values of the pixels within each of the fourfilter output images to the addition unit 183 and the division unit 184.

The addition unit 183 integrates the sum of the absolute values of thepixels of the respective filter output images, and supplies the obtainedintegrated value to the division unit 184.

The division unit 184 divides the sum of the absolute values of thepixels of each of the filter output images by the integrated valuesupplied from the addition unit 183, and supplies the obtained value tothe evaluation value calculation 185.

The evaluation value calculation 185 calculates the evaluation value ofthe epidermis orientation based on the calculation values calculated bythe division unit 184, and supplies the calculated evaluation value ofthe epidermis orientation to the texture evaluation unit 124.

[Texture Evaluation Process]

Next, a texture evaluation process executed by the image processingsystem 101 will be described with reference to the flow chart shown inFIG. 5.

In addition, this texture evaluation process is initiated, for example,when an instruction to execute the texture evaluation process is inputthrough an input unit not shown in the image processing system 101.

In step S1, the epidermis image capturing unit 111 captures theepidermis image. That is, the epidermis image capturing unit 111captures the epidermis of the skin of the portion to be evaluated in theevaluation target (e.g., cheeks, forehead, and so forth), and suppliesthe captured epidermis image to the image correction unit 141.

In step S2, the epidermis image processing unit 121 processes theepidermis image.

[Epidermis Image Processing]

Here, the epidermis image processing will be described in detail withreference to the flow chart shown in FIG. 6.

In step S21, the image correction unit 141 corrects the image. Forexample, shading distortion or lens distortion occurring on outer edgesof the epidermis image may be considered. Accordingly, the imagecorrection unit 141, for example, performs shading correction or lensdistortion correction on the epidermis image, or cuts out a centralregion of the epidermis image.

In addition, the image correction unit 141, for example, reduces thecorrected image in order to lower the processing cost.

In addition, hereinafter, unless otherwise noted, the size of thecorrected epidermis image corresponds to 160 vertical×120 horizontalpixels.

The image correction unit 141 supplies the corrected epidermis image tothe single channel extraction unit 142.

In step S22, the single channel extraction unit 142 extracts signalcomponents of a predetermined channel from the corrected epidermisimage. For example, the single channel extraction unit 142 extracts thesignal components of the blue (B) channel from the corrected epidermisimage. The single channel extraction unit 142 then supplies the singlechannel epidermis image composed of the extracted signal components tothe noise removal unit 143.

In addition, when a spectral camera is used as the epidermis imagecapturing unit 111, this processing may be omitted.

In step S23, the noise removal unit 143 removes the noise of the singlechannel epidermis image. For example, the noise removal unit 143 appliesa smoothing filter to the single channel epidermis image.

In particular, the noise removal unit 143, for example, applies an edgepreservation type smoothing filter to the single channel epidermis imagein order to remove random noise or texture components on the skin ridgesor skin grooves. For example, a bilateral filter of which the kernelsize corresponds to 3×3 pixels, σ_(space)=15, and σ_(color)=15 is usedas the edge preservation type smoothing filter.

Next, the noise removal unit 143, for example, applies an isolated pointremoval filter to the single channel epidermis image in order to removespecular reflection components or high brightness regions due to effectssuch as sweat glands. For example, a median filter of 3×3 pixels is usedas the isolated point removal filter.

In addition, since such a noise removal process significantly depends onthe capturing environment or performance of the epidermis imagecapturing unit 111, it is preferable that filters, parameters, and soforth to be applied be properly changed.

The noise removal unit 143 supplies the noise-removed epidermis imagethat is the single channel epidermis image with noise removed to thebinarization unit 151 of the epidermis pattern detection unit 122 andthe epidermis orientation analysis unit 174 of the acquired elementanalysis unit 123.

Accordingly, the epidermis image processing is finished.

Referring back to FIG. 5, in step S3, the epidermis pattern detectionunit 122 performs the epidermis pattern detection process.

[Epidermis Pattern Detection Process]

Here, details of the epidermis pattern detection process will bedescribed with reference to the flow chart shown in FIG. 7.

In step S41, the binarization unit 151 performs a binarization process.In particular, the binarization unit 151 binarizes the noise-removedepidermis image in order to perform segmentation on the skin ridges andskin grooves on the assumption that bright regions are protruding skinridges and dark regions are recessed skin grooves in the epidermis imageunder the uniform light source.

For example, the binarization unit 151 performs the adaptive localbinarization on the noise-removed epidermis image in order to furtherreduce effects of external light such as shading. For example, thebinarization unit 151 calculates the total sum using Gaussians asweights with respect to nearby regions and uses, as a threshold value, avalue obtained by subtracting a predetermined value bin_param from thetotal sum based on Equations (1a) and (1b) below, thereby binarizing thenoise-removed epidermis image.

$\begin{matrix}{{{I\left( {x,y} \right)} \leqq {{\sum\limits_{x,{y \in \Omega}}\left\lbrack {{G\left( {x,y} \right)} \cdot {I\left( {x,y} \right)}} \right\rbrack} - {{bin\_ param}\text{:~~}{I_{bin}\left( {x,y} \right)}}}} = {Ib}} & \left( {1a} \right) \\{{{I\left( {x,y} \right)} > {{\sum\limits_{x,{y \in \Omega}}\left\lbrack {{G\left( {x,y} \right)} \cdot {I\left( {x,y} \right)}} \right\rbrack} - {{bin\_ param}\text{:~~}{I_{bin}\left( {x,y} \right)}}}} = {Iw}} & \left( {1b} \right)\end{matrix}$

I(x, y) indicates the pixel value of the pixel of the coordinate(x, y)in the noise-removed epidermis image, G(x, y) indicates the Gaussianfunction, and I_(bin)(x, y) indicates the pixel value of the pixel ofthe coordinate (x, y) in the binarized epidermis image. In addition, Ibindicates the pixel value of black, and Iw indicates the pixel value ofwhite.

In addition, the value of bin_param is suitably set so as to properlyseparate the skin ridge regions and the skin groove regions within theepidermis image.

The binarization unit 151 performs binarization on all pixels of thenoise-removed epidermis image while shifting the block of interest byone pixel, thereby generating the binarized epidermis image. Thebinarization unit 151 then supplies the generated binarized epidermisimage to the labeling process unit 152.

In step S42, the labeling process unit 152 performs the 4-connected or8-connected labeling from the outside on the binarized epidermis image.Here, the labeling process unit 152 detects a region surrounded with awhite contour at the outermost side as one region, and ignores a blackregion or another region surrounded with a white contour even if it ispresent inside the region surrounded at the outermost side.

For example, as shown in FIG. 8, when the region A1 and the region A2including black regions within regions surrounded with white contoursare present in the binarized epidermis image, the region A1 and theregion A2 are first recognized individually by the 4-connected labelingprocess. Further, since black regions within the region A1 and theregion A2 are ignored, each of regions R1 and R2 having 3×3 pixels isrecognized as one region, and is given with the labeling.

Accordingly, for example, dark regions due to recesses or the likewithin the skin ridges are ignored, so that it is possible to correctlydetect the skin ridge regions.

In addition, hereinafter, regions given with the labeling by thelabeling process are referred to as labeling regions.

In addition, in the skin of general people, an interval between the skingrooves is 0.25 to 0.5 mm. Considering that shapes of the skin ridgesare usually triangles or quadrangles, the area of the skin ridge isthought to be about 0.031 to 0.25 mm².

Hence, the labeling process unit 152 obtains the proper range of thesize of the skin ridge in the epidermis image based on the size of theimage sensor of the epidermis image capturing unit 111 and so forth. Thelabeling process unit 152 then detects the region of a size within theobtained proper range among the detected labeling regions as the skinridge region.

In addition, the labeling process unit 152 counts the number of detectedskin ridge regions as the number of skin ridges N_(ridge).

The labeling process unit 152 supplies the epidermis pattern detectionresult indicating the detecting result of the number of skin ridgesN_(ridge) and the skin ridge regions to the epidermis size distributionanalysis unit 171, the epidermis shape distribution analysis unit 172,and the epidermis shape distribution analysis unit 173 of the acquiredelement analysis unit 123.

Referring back to FIG. 5, in step S4, the acquired element analysis unit123 performs the acquired element analysis process.

[Acquired Element Analysis Process]

Here, details of the acquired element analysis process will be describedwith reference to the flow chart shown in FIG. 9.

In step S61, the epidermis size distribution analysis unit 171 analyzesthe distribution of sizes of the epidermis patterns.

In particular, first, the epidermis size distribution analysis unit 171creates a histogram of sizes of the skin ridge regions. FIG. 10illustrates an example of the histogram of sizes (areas) of the skinridge regions. The horizontal axis and the vertical axis indicate thesizes of the skin ridge regions and the frequency frq_(n) of each bin ofthe histogram in FIG. 10, respectively.

Next, the epidermis size distribution analysis unit 171 calculates theaverage value H_(avg) of sizes of the skin ridge regions by means ofEquation 2 below.

$\begin{matrix}{H_{avg} = \frac{\sum\limits_{n}\left( {n \cdot {frq}_{n}} \right)}{\sum\limits_{n}\left( {frq}_{n} \right)}} & (2)\end{matrix}$

In addition, n indicates a median value of each bin.

In addition, the epidermis size distribution analysis unit 171calculates the variance H_(var) of sizes of the skin ridge regions bymeans of Equation 3 below.

$\begin{matrix}{H_{var} = \frac{\sum\limits_{n}\left( {\left( {n - H_{avg}} \right)^{2} \cdot {frq}_{n}} \right)}{\sum\limits_{n}\left( {frq}_{n} \right)}} & (3)\end{matrix}$

Further, the epidermis size distribution analysis unit 171 calculatesthe evaluation value of the epidermis size distribution Eeval_(size) inwhich the variance H_(var) is normalized within a range of 0 to 1 bymeans of the normalization curve shown in FIG. 11. Here, each ofSize_th_min and Size_th_max is threshold values that determine thenormalization curve in FIG. 11.

The evaluation value of the epidermis size distribution Eeval_(size)increases as the variance of sizes of the skin ridge regions H_(var)decreases. That is, the evaluation value of the epidermis sizedistribution Eeval_(size) increases as the variation in size of the skinridge regions decreases. Accordingly, the evaluation value of theepidermis size distribution Eeval_(size) becomes the index forindicating the uniformity of sizes of the skin ridge regions.

The epidermis size distribution analysis unit 171 supplies theevaluation value of the epidermis size distribution Eeval_(size) to thetexture evaluation unit 124.

In step S62, the epidermis shape distribution analysis unit 172 performsthe epidermis shape distribution analysis process 1.

[Epidermis Shape Distribution Analysis Process 1]

Here, details of the epidermis shape distribution analysis process 1 ofstep S62 will be described with reference to the flow chart in FIG. 12.

In step S81, the epidermis shape distribution analysis unit 172 selectsa reference region. That is, the epidermis shape distribution analysisunit 172 selects one skin ridge region that is not yet set as thereference region, and then sets the one skin ridge region as thereference region.

In step S82, the epidermis shape distribution analysis unit 172 selectsa comparison region. That is, the epidermis shape distribution analysisunit 172 selects one skin ridge region that is not used for comparisonwith the shape of the reference region, and then sets the one skin ridgeregion as the comparison region.

In step S83, the epidermis shape distribution analysis unit 172calculates the degree of difference in shape between the referenceregion and the comparison region.

For example, the epidermis shape distribution analysis unit 172quantifies the shapes of the reference region and the comparison regionusing the Hu moment invariant, and calculates the degree of differencein shape between the reference region and the comparison region based onthe quantified values.

Here, the Hu moment invariant will be described briefly.

First, the image moment M_(pq) indicating variance values of pixelswhere an origin point of the image is centered is calculated as inEquation 4 below.

$\begin{matrix}{M_{pq} = {\sum\limits_{x,y}\left( {{I\left( {x,y} \right)} \cdot x^{p} \cdot y^{q}} \right)}} & (4)\end{matrix}$

The image moment M_(pq) increases as higher pixel values scatter inlocations farther away from the origin point.

In addition, p and q in Equation 4 indicate weights in the X-axis andY-axis directions, respectively. Accordingly, the weight on the variancetoward the X-axis direction increases when the value p increases, andthe weight on the variance toward the Y-axis direction increases whenthe value q increases.

Next, the central moment (centroid moment) μ_(pq) indicating thevariance where centroids in the X-axis and Y-axis directions are takeninto consideration in the pixel values within the image is calculated asin Equation 5 below.

$\begin{matrix}{\mu_{pq} = {\sum\limits_{x,y}\left( {{I\left( {x,y} \right)} \cdot \left( {x - x_{c}} \right)^{p} \cdot \left( {y - y_{c}} \right)^{q}} \right)}} & (5)\end{matrix}$

Here, x_(c) and y_(c) are expressed as Equations (6a) and (6b) below,and indicate centroid positions in the X-axis and Y-axis directions,respectively.x _(c) =M ₁₀ /M ₀₀  (6a)y _(c) =M ₀₁ M ₀₁ /M ₀₀  (6b)

Finally, by normalizing the central moment μ_(pq) using the image momentM_(pq) in Equation 7, the normalized centroid moment η_(pq) is obtained.η_(pq)=μ_(pq) /M ₀₀ ^(((p+q)/2+1))  (7)

Since this normalization prevents the spreading state of the variancefrom affecting the moment value, the normalized centroid moment η_(pq)becomes invariant with respect to the parallel movement or rotationalmovement of an object within the image or image sizes.

The Hu moment invariant is a combination of the normalized centroidmoments η_(pq), and is defined as Equations (8a) to (8g) below.h ₁=η₂₀+η₀₂  (8a)h ₁=(η₂₀−η₀₂)²+4η₁₁ ²  (8b)h ₃=(η₃₀−3η₁₂)²+(3η₂₁−η₀₃)²  (8c)h ₄=(η₃₀+η₁₂)²+(η₂₁+η₀₃)²  (8d)h ₅=(η₃₀+3η₁₂)(η₃₀+η₁₂)[(η₃₀+η₁₂)²−3(η₂₁+η₀₃)²]  (8e)h ₆=(η₂₀+η₀₂)[(η₃₀+η₁₂)²−(η₂₁+η₀₃)²]+4η₁₁(η₃₀+η₁₂)(η₂₁+η₀₃)  (8f)h ₇=(3η₂₁−η₀₃)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²−(η₂₁+η₀₃)²]  (8g)

In addition, details of the Hu moment invariant are disclosed, forexample, in M-K. Hu. “Visual pattern recognition by moment invariants,”IRE Transaction on Information Theory, February, 1962, Volume 8, pp.179-187.

The epidermis shape distribution analysis unit 172 calculates the Humoment amount h^(A) _(i) of the reference region and the Hu momentamount h^(B) _(i) of the comparison region based on Equations (4) to(8g). However, in Equations (4) and (5), pixel values I_(bin)(x,y) ofthe binarized epidermis image are used instead of the pixel valuesI(x,y).

The epidermis shape distribution analysis unit 172 calculates the degreeof difference D(A,B) in shape between the reference region (region A)and the comparison region (region B) by means of Equation (9a).

$\begin{matrix}{{D\left( {A,B} \right)} = {\sum\limits_{i = 1}^{7}{{\frac{1}{m_{i}^{A}} - \frac{1}{m_{i}^{B}}}}}} & \left( {9a} \right)\end{matrix}$

Here, m^(A) _(i) and m^(B) _(i) indicate amounts represented by Equation(10) below.m ^(A) _(i)=sign(h ^(A) _(i))·log(h ^(A) _(i))m ^(B) _(i)=sign(h ^(B) _(i))·log(h ^(B) _(i))  (10)

In addition, the degree of difference D(A,B) may be calculated byEquation (9b) or (9c) instead of Equation (9a).

$\begin{matrix}{{D\left( {A,B} \right)} = {\sum\limits_{i = 1}^{7}{{m_{i}^{A} - m_{i}^{B}}}}} & \left( {9b} \right) \\{{D\left( {A,B} \right)} = {\sum\limits_{i = 1}^{7}{\frac{{m_{i}^{A} - m_{i}^{B}}}{m_{i}^{A}}}}} & \left( {9c} \right)\end{matrix}$

The degree of difference D(A,B) of any of Equations (9a) to (9c) alsodecreases when shapes of the reference region and the comparison regionbecome closer to each other.

In step S84, the epidermis shape distribution analysis unit 172integrates the degrees of difference. That is, the epidermis shapedistribution analysis unit 172 adds the newly calculated degree ofdifference to the integrated value of the degrees of difference of everyskin ridge region so far.

In step S85, the epidermis shape distribution analysis unit 172determines whether or not skin ridge regions that are not compared withthe reference region remain. When it is determined that skin ridgeregions not compared with the reference region remain, the processreturns to step S82.

Thereafter, in step S85, a process from step S82 to step S85 isrepeatedly carried out until it is determined that no skin ridge regionsnot compared with the reference region remain.

Meanwhile, in step S85, when it is determined that no skin ridge regionsnot compared with the reference region remain, the process proceeds tostep S86.

In step S86, the epidermis shape distribution analysis unit 172determines whether or not a skin ridge region that is not set as thereference region remains. When it is determined that a skin ridge regionthat is not set as the reference region remains, the process returns tostep S81.

Thereafter, in step S86, a process from steps S81 to S86 is repeatedlycarried out until it is determined that no skin ridge region that is notset as the reference region remains. Accordingly, the degree ofdifference is calculated for all combination of the skin ridge regions,and the accumulative added value of the degrees of difference is alsocalculated.

Meanwhile, in step S86, when it is determined that no skin ridge regionthat is not set as the reference region remains, the process proceeds tostep S87.

In step S87, the epidermis shape distribution analysis unit 172calculates the average of degrees of difference Diff_(avg) by means ofEquation (11) below.

$\begin{matrix}{{Diff}_{avg} = \frac{\sum\limits_{i = 0}^{N_{ridge} - 1}{\sum\limits_{j = {i + 1}}^{N_{ridge} - 1}{D\left( {R_{i},R_{j}} \right)}}}{N_{comp}}} & (11)\end{matrix}$

In addition, R_(i) and R_(j) indicate skin ridge regions of the labels iand j, respectively. Accordingly, the denominator of the right side ofEquation 11 is the accumulative added value of the degrees of differencein shape for all combinations of the skin ridge regions. In addition,N_(comp) indicates the number of comparisons of shapes of the skin ridgeregions, which is obtained by Equation 12 below.

$\begin{matrix}{N_{comp} = \frac{N_{ridge}\left( {N_{ridge} - 1} \right)}{2}} & (12)\end{matrix}$

In step S88, the epidermis shape distribution analysis unit 172calculates the evaluation value. In particular, the epidermis shapedistribution analysis unit 172 calculates the evaluation value of theepidermis shape distribution Eeval_(shape) in which the average of thedegrees of difference Diff_(avg) is normalized within a range of 0 to 1by means of the normalization curve shown in FIG. 13. Here, Shape_th_minand Shape_th_max are threshold values that determine the normalizationcurve in FIG. 13, respectively.

The evaluation value of the epidermis shape distribution Eeval_(shape)increases as the average of degrees of difference Diff_(avg) in shapebetween the skin ridge regions decreases. That is, the evaluation valueof the epidermis shape distribution Eeval_(shape) increases as thevariation in shape between the skin ridge regions decreases.Accordingly, the evaluation value of the epidermis shape distributionEeval_(shape) becomes the index for indicating the uniformity of shapesof the skin ridge regions.

The epidermis shape distribution analysis unit 172 supplies theevaluation value of the epidermis shape distribution Eeval_(shape) tothe texture evaluation unit 124.

Thereafter, the epidermis shape distribution analysis process 1 isfinished.

Referring back to FIG. 9, in step S63, the epidermis shape distributionanalysis unit 173 performs the epidermis shape distribution analysisprocess 2.

[Epidermis Shape Distribution Analysis Process 2]

Here, details of the epidermis shape distribution analysis process 2 instep S63 will be described with reference to the flow chart shown inFIG. 14.

In step S101, the epidermis shape distribution analysis unit 173 selectsa reference shape.

It is ideal for the shape of the skin ridge to usually be a triangle ora rhombus. On the other hand, a shape such as a bifurcated shape orelongated shape is not considered to be ideal.

The epidermis shape distribution analysis unit 173, for example, setsShape0 to Shape3 shown in FIG. 15 as the reference shapes. The referenceshapes Shape0 and Shape1 are a triangle and a rhombus and are close toideal shapes of the skin ridges, respectively. On the other hand, thereference shapes Shape2 and Shape3 are bifurcated and elongated shapesand are close to the shapes of the skin ridges that are not ideal,respectively.

The epidermis shape distribution analysis unit 173 selects one referenceshape that is not yet compared with the skin ridge regions.

In step S102, the epidermis shape distribution analysis unit 173 selectsa comparison region. That is, the epidermis shape distribution analysisunit 173 selects one skin ridge region that is not yet compared with thereference region, and sets the selected skin ridge region as thecomparison region.

In step S103, the epidermis shape distribution analysis unit 173calculates the degree of difference in shape between the reference shapeand the shape of the comparison region. In addition, to this calculationof the degree of difference, the same method as that of calculating thedegree of difference between the comparison region and the referenceregion of the skin ridge regions in step S83 of FIG. 12 described aboveis applied.

In step S104, the epidermis shape distribution analysis unit 173integrates the degrees of difference. That is, the epidermis shapedistribution analysis unit 173 adds the newly calculated degree ofdifference to the integrated value of the degrees of difference betweenthe current reference shape and each of the skin region regions so far.

In step S105, the epidermis shape distribution analysis unit 173determines whether or not a skin ridge region not compared with thecurrent reference shape remains. When it is determined that a skin ridgeregion not compared with the current reference shape remains, theprocess returns to step S102.

Thereafter, in step S105, the process from steps S102 to S105 isrepeatedly carried out until it is determined that that no skin ridgeregion not compared with the current reference shape remains.

On the other hand, in step S105, when it is determined that no skinridge region not compared with the current reference shape remains, theprocess proceeds to step S106.

In step S106, the epidermis shape distribution analysis unit 173determines whether or not a reference shape that is not comparedremains. When it is determined that a reference shape that is notcompared remains, the process returns to step S101.

Thereafter, in step S106, a process from steps S101 to S106 isrepeatedly carried out until it is determined that no reference shapethat is not compared remains. Accordingly, as shown in Equation 13, theaccumulative added value Diff_(I) of the degrees of difference in shapebetween each of the reference shapes and each of the skin ridge regionsis calculated.

$\begin{matrix}{{Diff}_{i} = {\sum\limits_{j = 0}^{N_{ridge}}{D\left( {S_{i},R_{j}} \right)}}} & (13)\end{matrix}$

In addition, S_(i) indicates the reference shape having the value i ofID.

On the other hand, in step S106, when it is determined that no referenceshape that is not compared remains, the process proceeds to step S107.

In step S107, the epidermis shape distribution analysis unit 173calculates the ratio of shapes of the skin ridge regions. In particular,the epidermis shape distribution analysis unit 173 calculates theepidermis shape distribution information ShapeRatio_(i) indicating theratio of shapes of the skin ridge regions by means of Equation 14.

$\begin{matrix}{{ShapeRatio}_{i} = \frac{{Diff}_{i}}{\sum\limits_{i = 0}^{N_{RS} - 1}{Diff}_{i}}} & (14)\end{matrix}$

Here, N_(RS) indicates the total number of reference shapes.

Accordingly, the epidermis shape distribution information ShapeRatio_(i)indicates the ratio of the skin ridge regions having the shapes close tothe reference shape of which the ID has the value i.

The epidermis shape distribution analysis unit 173 supplies theepidermis shape distribution information ShapeRatio_(i) to the textureevaluation unit 124.

Thereafter, the epidermis shape distribution analysis process 2 isfinished.

Referring back to FIG. 9, in step S64, the epidermis orientationanalysis unit 174 analyzes the orientation of the epidermis patterns.

In particular, the four-directional filtering unit 181 of the epidermisorientation analysis unit 174, for example, applies each of thefour-directional filters f₀ to f₃ shown in Equations (15a) to (15d) tothe noise-removed epidermis image as shown in Equation 16.

$\begin{matrix}{f_{0} = \begin{pmatrix}1 & 0 & {- 1} \\1 & 0 & {- 1} \\1 & 0 & {- 1}\end{pmatrix}} & \left( {15a} \right) \\{f_{1} = \begin{pmatrix}0 & 1 & 1 \\{- 1} & 0 & 1 \\{- 1} & {- 1} & 0\end{pmatrix}} & \left( {15b} \right) \\{f_{2} = \begin{pmatrix}1 & 1 & 1 \\0 & 0 & 0 \\{- 1} & {- 1} & {- 1}\end{pmatrix}} & \left( {15c} \right) \\{f_{3} = \begin{pmatrix}1 & 1 & 0 \\1 & 0 & {- 1} \\0 & {- 1} & {- 1}\end{pmatrix}} & \left( {15d} \right) \\{I_{HPFi} = {f_{i} \otimes I_{LPF}}} & (16)\end{matrix}$

In addition, the filter f₀, the filter f₁, the filter f₂, and the filterf₃ are edge extraction filters of 0° (horizontal direction), 45°, 90°(vertical direction), and 135°, respectively. In addition, I_(LPF)indicates the noise-removed epidermis image, and I_(HPFi) indicates thefilter output image obtained by applying the filter f_(i) to thenoise-removed epidermis image I_(LPF).

The four-directional filtering unit 181 supplies the filter output imageI_(HPFi) to the absolute value sum calculation unit 182.

Next, the absolute value sum calculation unit 182 calculates the sum ofabsolute values S_(i) of the pixel values I_(HPFi)(x,y) within the imagein each filter output image by means of Equation 17.

$\begin{matrix}{S_{i}{\sum\limits_{x,y}{{I_{HPFi}\left( {x,y} \right)}}}} & (17)\end{matrix}$

The absolute value sum calculation unit 182 supplies the obtained sum ofabsolute values S_(i) to the addition unit 183 and the division unit184.

The addition unit 183 integrates the sums of absolute values S_(i), andsupplies the obtained integrated value Σs_(i) to the division unit 184.

The division unit 184 divides each of the sums of absolute values S_(i)by the integrated value Σs_(i), and supplies the obtained values_(i)/Σs_(i) to the evaluation value calculation unit 185.

The evaluation value calculation unit 185 calculates the evaluationvalue of the epidermis orientation Eeval_(direction) by means ofEquation 18 below.

$\begin{matrix}{{Eeval}_{direction} = {\prod\limits_{i}\;{\exp\left( {{- {gain}} \cdot {{\frac{s_{i}}{\sum\limits_{i}s_{i}} - \frac{1}{4}}}} \right)}}} & (18)\end{matrix}$

In addition, gain is a gain value and is set as a predetermined value.

Here, when edge directions of the skin ridge regions are uniformlydistributed in four directions such as vertical, horizontal and slopeddirections (0°, 45°, 90°, and 135°), each si/Σsi is ¼, and theevaluation value of the epidermis orientation Eeval_(direction) is 1. Onthe other hand, when the edge directions of the skin ridge regions arenot uniformly distributed in the four directions, at least one of si/Σsiis not ¼, so that the evaluation value of the epidermis orientationEeval_(direction) is smaller than 1. Accordingly, the evaluation valueof the epidermis orientation Eeval_(direction) becomes the index forindicating the uniformity of distribution in the edge directions of theskin ridge regions.

The evaluation value calculation unit 185 supplies the evaluation valueof the epidermis orientation Eeval_(direction) to the texture evaluationunit 124.

Thereafter, the acquired element analysis process is finished.

Referring back to FIG. 5, in step S5, the texture evaluation unit 124calculates the texture evaluation value.

For example, the texture evaluation unit 124 calculates the textureevaluation value eval1 _(total) by means of Equation 19 below.eval1_(total)=Eeval_(size)×Eeval_(shape)×Eeval_(direction)  (19)

The texture evaluation value eval1 _(total) increases when each of theuniformity of sizes of the skin ridges, the uniformity of shapes of theskin ridges, and the uniformity of distribution of the skin ridgeorientations increases, that is, when the textures are generally good(when the texture uniformity is high). In addition, the uniformity ofsizes of the skin ridges, the uniformity of shapes of the skin ridges,and the uniformity of distribution of the skin ridge orientations arechanged in an acquired manner by aging, health, skin care and so forth.Accordingly, the texture evaluation value eval1 _(total) becomes theindex for evaluating the uniformity of skin textures that are changed inan acquired manner.

The uniformity of textures greatly influences the aesthetic view of skinas the fineness of the texture does. That is, the appearance of the skinis worse when the textures are not generally good even when the texturesare fine, whereas the appearance of the skin is better when the texturesare generally good even when the textures are not fine.

In addition, instead of the texture evaluation value eval1 _(total), oralong with the texture evaluation value eval1 _(total), the textureevaluation value eval2 _(total) may be calculated as in Equation 20below.eval2_(total)=Eeval_(size)×Eeval_(shape)×Eeval_(direction)×ShapeRatio_(ideal)  (20)

ShapeRatio_(ideal), for example, is calculated as in Equation (21)below.ShapeRatio_(ideal)=ShapeRatio₀×ShapeRatio₁  (21)

ShapeRatio₀ is the ShapeRatio with respect to the reference shape Shape0of the triangle in FIG. 15, and ShapeRatio₁ is the ShapeRatio withrespect to the reference shape Shape1 of the rhombus in FIG. 15. Thatis, ShapeRatio_(ideal) indicates the ratio at which the skin ridgeregions have the ideal shape such as a triangle or a rhombus.

Accordingly, in addition to the uniformity of textures, the textureevaluation value eval2 _(total) increases when the ratio of skin ridgeshaving the ideal shapes increases. Thus, the texture evaluation valueeval2 _(total) becomes an index that is compared with the textureevaluation value eval1 _(total) to evaluate the acquired elements thataffect the texture state of the skin in further detail.

The texture evaluation unit 124 supplies the evaluation result of thetexture state of the skin to the evaluation result presentation unit125. Here, the texture evaluation unit 124 supplies not only the textureevaluation values eval1 _(total) and eval2 _(total) but also eachevaluation value used for calculating the texture evaluation valueseval1 _(total) and eval2 _(total) to the evaluation result presentationunit 125.

In step S6, the evaluation result presentation unit 125 presents theevaluation result. In addition, with respect to specific examples of themethod of presenting the evaluation result, the second embodiment of thepresent technology will be described.

Thereafter, the texture evaluation process is finished.

As described above, based on the uniformity of textures, which is anacquired element indicating the texture state of the skin, and theshapes of skin ridges, it is possible to evaluate the texture state ofthe skin. As a result, it is possible to more correctly evaluate thetexture state of the skin.

<2. Second Embodiment>

Next, the second embodiment of the present technology will be describedwith reference to FIGS. 16 to 23. In the second embodiment, not only theacquired elements but also the inherent elements are analyzed toevaluate the texture state of the skin.

[Configuration Example of Image Processing System 201]

FIG. 16 is a block diagram illustrating the configuration example of theimage processing system 201 in accordance with the second embodiment ofthe present technology. In addition, like parts corresponding to thosein FIG. 1 are designated by like numerals. With respect to parts to beprocessed in the same manner, the repeated description thereof isproperly omitted.

The image processing system 201 differs from the image processing system101 of FIG. 1 in that the image processing unit 211 is disposed insteadof the image processing unit 112. In addition, the image processing unit211 differs from the image processing unit 112 in that the inherentelement analysis unit 221 is additionally disposed and the textureevaluation unit 222 and the evaluation result presentation unit 223 aredisposed instead of the texture evaluation unit 124 and the evaluationresult presentation unit 125.

The inherent element analysis unit 221, as will be described later,performs the inherent element analysis on the inherent elements amongelements indicating the texture state of the skin based on the detectionresult of the epidermis patterns. The inherent element analysis unit 221supplies the analysis result to the texture evaluation unit 222.

The texture evaluation unit 222 evaluates the texture state of the skinof the evaluation target based on the analysis result from the acquiredelement analysis unit 123 and the analysis result from the inherentelement analysis unit 221, and supplies the evaluation result to theevaluation result presentation unit 223.

The evaluation result presentation unit 223 causes the display unit 113to display the information indicating the evaluation result of thetexture state of the skin of the evaluation target.

[Configuration Example of Inherent Element Analysis Unit 221]

FIG. 17 is a block diagram illustrating the configuration example of theinherent element analysis unit 221.

The inherent element analysis unit 221 includes a skin ridge numberanalysis unit 241.

The skin ridge number analysis unit 241 obtains the detection result ofthe epidermis patterns from the labeling process unit 152 of theepidermis pattern detection unit 122. The skin ridge number analysisunit 241 then calculates the evaluation value of the number of skinridges that is the index for indicating the fineness of the skintexture, based on the number of skin ridges N_(ridge) included in thedetection result of the epidermis patterns. The skin ridge numberanalysis unit 241 supplies the calculated evaluation value of the numberof skin ridges to the texture evaluation unit 222.

In addition, it is known that the number of skin ridges N_(ridge) isinherent and that variation due to aging, health, skin care and so forthis small. Accordingly, the evaluation value of the number of skin ridgesbecomes the index for evaluating the inherent property of the texturestate of the skin.

[Texture Evaluation Process]

Next, the texture evaluation process carried out by the image processingsystem 201 will be described with reference to the flow chart of FIG.18.

In addition, this process, for example, is initiated when theinstruction to execute the texture evaluation process is input throughan input unit not shown in the image processing system 201.

Processing from step S201 to step S204 is same as that from step S1 tostep S4 of FIG. 5, and the repeated description thereof is omitted.

In step S205, the inherent element analysis unit 221 performs theinherent element analysis process.

[Inherent Element Analysis Process]

Here, details of the inherent element analysis process will be describedwith reference to the flow chart of FIG. 19.

In step S221, the skin ridge number analysis unit 241 of the inherentelement analysis unit 221 analyzes the number of skin ridges. Inparticular, the skin ridge number analysis unit 241 calculates theevaluation value of the number of skin ridges Eeval_(num) that thenumber of skin ridges N_(ridge) are detected by the labeling processunit 152 of the epidermis pattern detection unit 122 and are normalizedwithin a range of 0 to 1 by the normalization curve shown in FIG. 20.Here, each of Num_th_min and Num_th_max is threshold values thatdetermine the normalization curve in FIG. 20.

The evaluation value of the number of skin ridges Eeval_(num) increaseswhen the number of skin ridges N_(ridge) increases. In addition, sincethe number of skin ridges N_(ridge) indicates the number of skin ridgeswithin the epidermis image (i.e., the number of skin ridges per unitarea), it may be said that the texture of the skin is finer when thenumber of skin ridges N_(ridge) increases. Accordingly, the evaluationvalue of the number of skin ridges Eeval_(num) becomes an indexindicating the fineness of the skin.

The skin ridge number analysis unit 241 supplies the evaluation value ofthe skin ridges Eeval_(num) to the texture evaluation unit 222.

Thereafter, the inherent element analysis process is finished.

Referring back to FIG. 18, in step S206, the texture evaluation unit 222calculates the texture evaluation value.

For example, the texture evaluation unit 222 calculates the textureevaluation value eval3 _(total) by means of Equation (22) below.eval3_(total)=α_(inherent)×Eeval_(num)+(1−α_(inherent))×eval1_(total)  (22)

In addition, α_(inherent) is the weight indicating the ratio at whichthe inherent element affects the texture state of the skin, and is setwithin a range of 0 to 1. In addition, it is preferable that such weightα_(inherent) be automatically set to a proper value in accordance withelements estimated to affect the texture state such as sex, race, andage, for example.

In addition, the texture evaluation value eval1 _(total) of Equation(22) is calculated based on Equation (19) as described above.

The texture evaluation value eval3 _(total) is one in which not only theacquired element property of the texture state of the skin but also theinherent property such as texture fineness is evaluated. Accordingly,the texture evaluation value eval3 _(total) becomes the index that iscompared with the texture evaluation value eval1 _(total) to evaluatethe texture state of the skin in further detail.

In addition, in Equation (22), the texture evaluation value eval2_(total) of Equation (20) may be used instead of the texture evaluationvalue eval1 _(total).

The texture evaluation unit 222 supplies the evaluation result of thetexture state of the skin to the evaluation result presentation unit223. Here, the texture evaluation unit 222 supplies not only the textureevaluation value eval3 _(total) but also each evaluation value used forcalculating the texture evaluation value eval3 _(total) to theevaluation result presentation unit 223.

In step S207, the evaluation result presentation unit 223 presents theevaluation result.

For example, the evaluation result presentation unit 223 causes thedisplay unit 113 to display the screen shown in FIG. 21. In the presentexample, a radar chart that individually compares the uniformity ofsizes of the skin ridges previous and current times, the uniformity ofshapes of the skin ridges previous and current times, the uniformity ofdistribution of the skin ridge orientations previous and current times,and the evaluation values of fineness of the texture previous andcurrent times is illustrated. The evaluation value of the epidermis sizedistribution Eeval_(size), the evaluation value of the epidermis shapedistribution Eeval_(shape), the evaluation value of the epidermisorientation Eeval_(direction), and the evaluation value of the number ofskin ridges Eeval_(num) are used as the values of the radar chart.

In addition, in the present example, the change in comprehensivedetermination of the texture states of the skin previous and currenttimes is shown. Such a comprehensive determination value is determined,for example, based on the texture evaluation value eval3 _(total) usingthe table shown in FIG. 22. That is, the comprehensive determinationvalue is given with a higher evaluation value when the textureevaluation value eval3 _(total) increases.

Accordingly, the evaluation target may recognize the texture state ofthe skin of the evaluation target at once, and also recognize the changein evaluation value from the previous time.

In addition, as shown in FIG. 23, the circular graph indicating thedistribution of shapes of the skin ridges based on the epidermis shapedistribution information ShapeRatio_(i) may also be presented.

Thereafter, the texture evaluation process is finished.

As described above, the acquired property and the inherent property ofthe texture state of the skin may be separately evaluated. In addition,it is possible to evaluate the texture state of the skin in furtherdetail based on both of the acquired property and the inherent property.As a result, it is possible to more correctly evaluate the texture stateof the skin.

<3. Third Embodiment>

Next, the third embodiment of the present technology will be describedwith reference to FIGS. 24 to 29. In the third embodiment, not only theepidermis state but also the dermis state is analyzed to evaluate thetexture state of the skin.

[Configuration Example of Image Processing System 301]

FIG. 24 is a block diagram illustrating a configuration example of theimage processing system 301 in accordance with the third embodiment ofthe present technology. In addition, like parts corresponding to thosein FIG. 16 are designated by like numerals. With respect to parts to beprocessed in the same manner, the repeated description thereof isproperly omitted.

The image processing system 301, as will be described later, detects notonly the epidermis patterns but also dermis patterns to perform the skinevaluation.

The image processing system 301 differs from the image processing system201 of FIG. 16 in that the dermis image capturing unit 311 isadditionally disposed and the image processing unit 312 is disposedinstead of the image processing unit 211. In addition, the imageprocessing unit 312 differs from the image processing unit 211 in thatthe dermis image processing unit 321 and the dermis pattern detectionunit 322 are additionally disposed, and the inherent element analysisunit 323, the texture evaluation unit 324 and the evaluation resultpresentation unit 325 are disposed instead of the inherent elementanalysis unit 221, the texture evaluation unit 222 and the evaluationresult presentation unit 223.

The dermis image capturing unit 311, as will be described later,captures the dermis of the skin of the evaluation target, and suppliesthe captured dermis image to the dermis image processing unit 321 of theimage processing unit 312.

The dermis image processing unit 321 has the same configuration as theepidermis image processing unit 121 shown in FIG. 2. Accordingly, thedermis image processing unit 321 performs image correction, singlechannel extraction, and noise removal on the dermis image. The dermisimage processing unit 321 then supplies the noise-removed dermis imageto the dermis pattern detection unit 322 and the inherent elementanalysis unit 323.

The dermis pattern detection unit 322 has the same configuration as theepidermis pattern detection unit 122 shown in FIG. 2. Accordingly, thedermis pattern detection unit 322 performs binarization and labelingprocess on the noise-removed dermis image to detect patterns of thedermis within the dermis image (hereinafter referred to as dermispatterns).

Here, an example of the dermis patterns detected by the dermis patterndetection unit 322 will be described with reference to FIG. 25.

FIG. 25 is a cross-sectional diagram schematically illustrating skintissues of the skin of a human.

The skin tissues roughly consist of the epidermis 351 and the dermis352. The epidermis 351 consists of a horny layer 361, a clear layer 362,a granular layer 363, a spinous layer 364, a basal layer 365, and abasal film 366. Meanwhile, the dermis 352 consists of a papillary layer371, a subpapillary layer 372, and a reticular layer (not shown).

The epidermis 351 and the dermis 352 have different constitutionaltissues from each other, and the tissues such as collagen or elasticfiber, which are not present in the epidermis 351, are present in thedermis 352. Accordingly, optical characteristics in the epidermis 351and the dermis 352 are different from each other. For example, theepidermis 351 has a high optical transparency with respect to red lightof visible lights or light in an optical band having a longer wavelengththan the near-infrared light whereas the dermis 352 has a low opticaltransparency with respect to light in the same wavelength range.

The dermis image capturing unit 311, as will be described later, usesthe difference in optical characteristics of the epidermis 351 and thedermis 352 to capture the dermis 352. The dermis pattern detection unit322 detects, as the dermis patterns, regions of elevated patterns havingirregular shapes configured mainly by the papillary layer 371 in contactwith the epidermis tissue by means of the basal film 366 (hereinafterreferred to as papillary layer regions).

In addition, the dermis pattern detection unit 322 counts the number ofpapillary layer regions within the dermis image (hereinafter referred toas the number of dermis patterns). The dermis pattern detection unit 322then supplies the dermis pattern detection result indicating thedetection result of the number of dermis patterns and the papillarylayer regions to the inherent element analysis unit 323.

The inherent element analysis unit 323, as will be described later,performs the inherent element analysis among elements indicating thetexture state of the skin based on the epidermis pattern detectionresult and the dermis pattern detection result. The inherent elementanalysis unit 323 supplies the analysis result to the texture evaluationunit 324.

The texture evaluation unit 324 evaluates the texture state of the skinof the evaluation target based on the analysis result from the acquiredelement analysis unit 123 and the analysis result from the inherentelement analysis unit 323, and supplies the evaluation result to theevaluation result presentation unit 325.

The evaluation result presentation unit 325 causes the display unit 113to display the evaluation result of the texture state of the skin of theevaluation target.

[Configuration Example of Dermis Image Capturing Unit 311]

FIG. 26 illustrates the configuration example of the dermis imagecapturing unit 311.

The dermis image capturing unit 311 has, as an irradiation opticalsystem, a light source 401, an optical lens 402, and an irradiationportion polarization plate 403. For example, any light source such as anLED may be used as the light source 401. However, it is preferable thata light source emitting long wavelength light such as near-infraredlight that transmits the epidermis and is scattered in the dermis beused as the light source 401. Accordingly, it is possible to obtaintissue patterns using optical characteristics such as scattering orbirefringence in the tissues below the epidermis.

In addition, the dermis image capturing unit 311 has, as an imagingoptical system, a capturing element that receives light 404 (forexample, a CCD image sensor, a CMOS image sensor, and so forth), animaging lens group 405, and a light receiving portion polarization plate406. In addition, a half-mirror 407 is disposed on an optical pathbetween the irradiation optical system and the imaging optical system,and is orthogonal with respect to the irradiation optical system and theimaging optical system.

In the dermis image capturing unit 311, the irradiation light from thelight source 401 is irradiated on the skin while the vibration directionis limited to one direction by the irradiation portion polarizationplate 403. In addition, although the light receiving portionpolarization plate 406 is disposed in the imaging optical system, thislight receiving portion polarization plate is configured such that thevibration direction is orthogonal to the irradiation portionpolarization plate 403. Accordingly, the simple reflected light in theepidermis tissue is shielded by the light receiving portion polarizationplate 406 because the vibration direction is caused to be orthogonal tothe light receiving portion polarization plate 406.

Irradiation lights irradiated on the skin from the irradiation opticalsystem reach as far as deep skin tissues (for example, the dermistissue), and the polarization is resolved by scattering or birefringenceoccurring due to various tissues. These lights transmit the half-mirror407 as backscattered lights to be guided to the imaging optical system.However, the polarization is resolved as described above, so that thelights transmit the light receiving portion polarization plate 406 toreach as far as the capturing element 404.

Accordingly, it is possible to capture the patterns having the irregularshapes of the dermis by separating the reflected and scattered lights inthe epidermis tissues from the lights having different phases that havepassed through the birefringence tissues (dermis tissues).

The capturing element 404 supplies the obtained dermis image to thedermis image processing unit 321.

[Configuration Example of Inherent Element Analysis Unit 323]

FIG. 27 is a block diagram illustrating the configuration example of theinherent element analysis unit 323. In addition, like partscorresponding to those in FIG. 17 are designated by like numerals, andwith respect to parts to be processed in the same manner, the repeateddescription thereof is properly omitted.

The inherent element analysis unit 323 differs from the inherent elementanalysis unit 221 of FIG. 17 in that the dermis pattern number analysisunit 421, the dermis size distribution analysis unit 422, the dermisshape distribution analysis unit 423, and the dermis orientationanalysis unit 424 are additionally disposed.

The dermis pattern number analysis unit 421 performs the same process asthe skin ridge number analysis unit 241 based on the dermis patterndetection result. That is, the dermis pattern number analysis unit 421calculates the evaluation value of the number of dermis patterns that isthe index for indicating the fineness of the skin texture based on thenumber of dermis patterns included in the dermis pattern detectionresult. The dermis pattern number analysis unit 421 supplies thecalculated evaluation value of the number of dermis patterns to thetexture evaluation unit 324.

The dermis size distribution analysis unit 422 analyzes the distributionof sizes of the dermis patterns by performing the same process as theepidermis size distribution analysis unit 171 of the acquired elementanalysis unit 123 of FIG. 3 based on the dermis pattern detectionresult. In particular, the dermis size distribution analysis unit 422analyzes the distribution of sizes of the papillary layer regions, andcalculates the evaluation value of the dermis size distributionindicating the uniformity of sizes of the papillary layer regions. Thedermis size distribution analysis unit 422 supplies the calculatedevaluation value of the dermis size distribution to the textureevaluation unit 324.

The dermis shape distribution analysis unit 423 analyzes thedistribution of shapes of the dermis patterns by performing the sameprocess as the epidermis shape distribution analysis unit 172 of theacquired element analysis unit 123 of FIG. 3 based on the dermis patterndetection result. In particular, the dermis shape distribution analysisunit 423 analyzes the distribution of shapes of the papillary layerregions, and calculates the evaluation value of the dermis shapedistribution indicating the uniformity of shapes of the papillary layerregions. The dermis shape distribution analysis unit 423 supplies thecalculated evaluation value of the dermis shape distribution to thetexture evaluation unit 324.

The dermis orientation analysis unit 424 analyzes the orientation of thedermis patterns by performing the same process as the epidermisorientation analysis unit 174 of the acquired element analysis unit 123of FIG. 3 based on the noise-removed dermis image. In particular, thedermis orientation analysis unit 424 analyzes the distribution of edgedirections of the papillary layer regions, and calculates the evaluationvalue of the dermis orientation indicating the uniformity ofdistribution of edge directions of the papillary layer regions. Thedermis orientation analysis unit 424 supplies the calculated evaluationvalue of the dermis orientation to the texture evaluation unit 124.

In addition, it is known that the dermis patterns formed by thepapillary layers or the like have many inherent elements and the changein dermis patterns due to aging, health, skin care and so forth issmall. Accordingly, the evaluation value of the number of dermispatterns, the evaluation value of the dermis size distribution, theevaluation value of the dermis shape distribution, and the evaluationvalue of the dermis orientation become indexes for evaluating inherentproperties of the texture state of the skin.

[Texture Evaluation Process]

Next, the texture evaluation process carried out by the image processingsystem 301 will be described with reference to the flow chart of FIG.28.

In addition, for example, this process is initiated when the instructionto carry out the texture evaluation process is input through an inputunit not shown in the image processing system 301.

In step S301, the epidermis image is captured in the same manner as instep S1 of FIG. 5.

In step S302, the dermis image capturing unit 311 captures the dermisimage. That is, the dermis image capturing unit 311, as described abovewith reference to FIG. 26, captures the dermis of the skin of theevaluation target, and supplies the captured dermis image to the dermisimage processing unit 321.

In addition, it is preferable that the capturing interval between theepidermis image and the dermis image be set to as short a time aspossible and capturing ranges of the epidermis image and the dermisimage be set to be almost equal to each other. In addition, the order ofcapturing the epidermis image and the dermis image may be reversed.

In steps S303 to S305, the same process as in steps S2 to S4 of FIG. 5is performed. Through this process, the evaluation value of theepidermis size distribution Eeval_(size), the evaluation value of theepidermis shape distribution Eeval_(shape), the epidermis shapedistribution information ShapeRatio_(i), the evaluation value of theepidermis orientation Eeval_(direction), and the number of skin ridgesN_(ridge) are obtained, and are then supplied to the texture evaluationunit 324.

In step S306, the dermis image processing unit 321 performs the dermisimage processing. That is, the dermis image processing unit 321performs, on the dermis image, the same process as that performed on theepidermis image by the epidermis image processing unit 121 in step S2 ofFIG. 5 described above. The dermis image processing unit 321 thensupplies the noise-removed dermis image to the dermis pattern detectionunit 322 and the inherent element analysis unit 323.

In step S307, the dermis pattern detection unit 322 performs the dermispattern detection process. That is, the dermis pattern detection unit322 performs, on the noise-removed dermis pattern, the same process asthat performed on the noise-removed epidermis image by the epidermispattern detection unit 122 in step S3 of FIG. 5 described above.

Accordingly, the dermis pattern detection unit 322 detects a pluralityof papillary layer regions from the noise-removed dermis image. Inaddition, the dermis pattern detection unit 322 counts the number ofdermis patterns N_(dermis) that is the number of papillary layer regionswithin the dermis image. The dermis pattern detection unit 322 suppliesthe dermis pattern detection result indicating the detection result ofthe number of dermis patterns and the papillary layer regions to theinherent element analysis unit 323.

In step S308, the inherent element analysis unit 323 performs theinherent element analysis process.

[Inherent Element Analysis Process]

Here, details of the inherent element analysis process will be describedwith reference to the flow chart of FIG. 29.

In step S331, the number of skin ridges is analyzed in the same manneras in the process of step S221 of FIG. 19 described above. The skinridge number analysis unit 241 supplies the evaluation value of thenumber of skin ridges Eeval_(num) to the texture evaluation unit 324.

In step S332, the dermis pattern number analysis unit 421 analyzes thenumber of dermis patterns. In particular, the dermis pattern numberanalysis unit 421 calculates the evaluation value of the number ofdermis patterns Deval_(num) using the number of dermis patternsN_(dermis) by performing the same process as the case of calculating theevaluation value of the number of skin ridges Eeval_(num) by the skinridge number analysis unit 241 in step S221 of FIG. 19.

Accordingly, the evaluation value of the number of dermis patternsDeval_(num) increases when the number of dermis patterns N_(dermis)increases in the same manner as the evaluation value of the number ofskin ridges Eeval_(num). In addition, since the number of dermispatterns N_(dermis) indicates the number of dermis patterns within thedermis image (i.e., the number of dermis patterns per unit area), it maybe said that the skin texture is finer when the number of dermispatterns N_(dermis) increases. Accordingly, the evaluation value of thenumber of dermis patterns Deval_(num) becomes the index for indicatingthe fineness of the skin texture.

The dermis pattern number analysis unit 421 supplies the evaluationvalue of the number of dermis patterns Deval_(num) to the textureevaluation unit 324.

In step S333, the dermis size distribution analysis unit 422 analyzesthe distribution of sizes of the dermis patterns. In particular, thedermis size distribution analysis unit 422 calculates the evaluationvalue of the dermis size distribution Deval_(size) based on thedetection result of the papillary layer regions by performing the sameprocess as the case of calculating the evaluation value of the epidermissize distribution Eeval_(size) by the epidermis size distributionanalysis unit 171 in step S61 of FIG. 9.

Accordingly, the evaluation value of the dermis size distributionDeval_(size) increases when the variance in sizes of the papillary layerregions is smaller in the same manner as the evaluation value of theepidermis size distribution Eeval_(size). That is, the evaluation valueof the dermis size distribution Deval_(size) increases when thevariations in size of the papillary layer regions are smaller.Accordingly, the evaluation value of the dermis size distributionDeval_(size) becomes the index for indicating the uniformity of sizes ofthe papillary layer regions.

The dermis size distribution analysis unit 422 supplies the evaluationvalue of the dermis size distribution Deval_(size) to texture evaluationunit 324.

In step S334, the dermis shape distribution analysis unit 423 performsthe dermis shape distribution analysis process. In particular, thedermis shape distribution analysis unit 423 calculates the evaluationvalue of the dermis shape distribution Deval_(shape) based on thedetection result of the papillary layer regions by performing the sameprocess as the case of calculating the evaluation value of the epidermisshape distribution Eeval_(shape) by the epidermis shape distributionanalysis unit 172 in the epidermis shape distribution analysis process 1of FIG. 12.

Accordingly, the evaluation value of the dermis shape distributionDeval_(shape) increases when the variations in shape of the papillarylayer regions are smaller in the same manner as the evaluation value ofthe epidermis shape distribution Eeval_(shape). Accordingly, theevaluation value of the dermis shape distribution Deval_(shape) is theindex for indicating the uniformity of shapes of the papillary layerregions.

The dermis shape distribution analysis unit 423 supplies the evaluationvalue of the dermis shape distribution Deval_(shape) to the textureevaluation unit 324.

In step S335, the dermis orientation analysis unit 424 analyzes theorientation of dermis patterns. In particular, the dermis orientationanalysis unit 424 calculates the evaluation value of the dermisorientation Deval_(direction) based on the noise-removed dermis image byperforming the same process as the case of calculating the evaluationvalue of the epidermis orientation Eeval_(direction) by the epidermisorientation analysis unit 174 in step S64 of FIG. 9.

Accordingly, the evaluation value of the dermis orientationDeval_(direction) increases when the edge directions of the papillarylayer regions are uniformly distributed in four directions such asvertical, horizontal and sloped directions (0°, 45°, 90°, and 135°) inthe same manner as the evaluation value of the epidermis orientationEeval_(direction). Accordingly, the evaluation value of the dermisorientation Deval_(direction) is the index for indicating the uniformityof the distribution of edge directions of the papillary layer regions.

The dermis orientation analysis unit 424 supplies the evaluation valueof the dermis orientation Deval_(direction) to the texture evaluationunit 324.

Referring back to FIG. 28, in step S309, the texture evaluation unit 324calculates the texture evaluation value.

For example, the texture evaluation unit 324 calculates the textureevaluation value eval4 _(total) by means of Equation (23) below.eval4_(total)=α_(inherent)×Eeval_(num)×Deval_(num)×Deval_(size)×Deval_(shape)×Deval_(direction)+(1−α_(inherent))×eval1_(total)  (23)

In addition, α_(inherent) and eval1 _(total) are same as those ofEquation (22) described above.

The texture evaluation value eval4 _(total) is the value compared withthe texture evaluation value eval3 _(total) of Equation (22) to evaluatenot only the epidermis state but also the dermis state. Accordingly, thetexture evaluation value eval4 _(total) is the index that is comparedwith the texture evaluation value eval3 _(total) to evaluate the texturestate of the skin in further detail.

In addition, in Equation (23), the texture evaluation value eval2_(total) of Equation (20) may be used instead of the texture evaluationvalue eval1 _(total).

The texture evaluation unit 324 supplies the evaluation result of thetexture state of the skin to the evaluation result presentation unit223. Here, the texture evaluation unit 324 supplies not only the textureevaluation value eval4 _(total) but also each evaluation value used forcalculating the texture evaluation value eval4 _(total) to theevaluation result presentation unit 325.

In step S307, the evaluation result is presented in the same manner asthe process of step S207 of FIG. 18.

Thereafter, the texture evaluation process is finished.

As described above, it is possible to evaluate the texture state of theskin in further detail based on the dermis state in addition to theepidermis state. As a result, it is possible to more correctly evaluatethe texture state.

<4. Modification>

Hereinafter, modification examples of the embodiments of the presenttechnology will be described.

MODIFICATION EXAMPLE 1

The method of calculating the degree of difference (or the degree ofsimilarity) in shape between the skin ridge regions or the papillarylayer regions is not limited to the methods described above withreference to FIG. 12, but may employ any methods. For example, asdescribed below, Dynamic Programming (DP) matching may be employed tocalculate the degree of similarity of shapes.

First, contour extraction, thinning, and vectorization are performed onthe region A to obtain the vector characteristic amount A indicated inEquations (24) and (25).A={a ₁ ,a ₂ , . . . ,a _(m)}  (24)a _(i)=(x _(is) ,y _(is) ,x _(ie) ,y _(ie))  (25)

In addition, x_(is) and y_(is) are coordinates of the start points ofthe vector a_(i), and x_(ie) and y_(ie) are coordinates of the endpoints of the vector a_(i).

In addition, the same process is also performed on the region B forcomparison with the region A to obtain the vector characteristic amountB indicated in Equations (26) and (27).B={b ₁ ,b ₂ , . . . ,b _(m)}  (26)b _(j)=(x _(js) ,y _(js) ,x _(je) ,y _(je))  (27)

In addition, x_(js) and y_(js) are coordinates of the start points ofthe vector b_(j), and x_(je) and y_(je) are coordinates of the endpoints of the vector b_(j).

Next, the degree of similarity h(a_(i), b_(j)) between one componentvector a_(i) of the region A and one component vector b_(j) of theregion B is calculated as in Equations (28) and (29) below.

$\begin{matrix}{{h\left( {a_{i},b_{j}} \right)} = {\min\left\{ \begin{matrix}{{h\left( {a_{i - 1},b_{j - 1}} \right)} + {2{d\left( {a_{i},b_{j}} \right)}}} \\{{h\left( {a_{i - 1},b_{j}} \right)} + {d\left( {a_{i},b_{j}} \right)}} \\{{h\left( {a_{i},b_{j - 1}} \right)} + {d\left( {a_{i},b_{j}} \right)}}\end{matrix} \right.}} & (28) \\{{d\left( {a_{i},b_{j}} \right)} = {{a_{i} - b_{j}}}} & (29)\end{matrix}$

For example, the degree of similarity S(A,B) between the region A andthe region B is calculated, for example, as in Equation (30) below.

$\begin{matrix}{{S\left( {A,B} \right)} = {\sum\limits_{ij}{h\left( {a_{i},b_{j}} \right)}}} & (30)\end{matrix}$

For example, it is possible to use this degree of similarity S(A,B)instead of the degree of difference D(A,B) calculated in Equations (9a)to (9c) described above.

MODIFICATION EXAMPLE 2

The process of classifying the shapes of the skin ridge regions in theepidermis shape distribution analysis process 2 of FIG. 14 is notlimited to the method described above. For example, Support VectorMachine (SVM) and so forth may be employed to perform the learning-basedshape classification using reference shapes. Alternatively, for example,clustering schemes such as K-means method without using the referenceshape may be employed to perform the shape classification.

MODIFICATION EXAMPLE 3

The process of analyzing edge directions of the skin ridge regions orpapillary layer regions is not limited to the method described abovemethod. For example, a fast Fourier transformation (FFT) may beperformed on the noise-removed dermis image or the noise-removedepidermis image to obtain the histogram with respect to each angle fromthe spectral image of the noise-removed dermis image or thenoise-removed epidermis image, and the distribution of the edgedirections of the skin ridge regions or the papillary layer regions maybe analyzed based on the shapes of the histogram.

MODIFICATION EXAMPLE 4

The configuration of the dermis image capturing unit 311 of FIG. 26 ismerely an example, and other configurations may be employed for thesame. For example, the dermis image may be captured by the capturingdevice using a micro lens array (MLA) employed for biometricauthentication such as fingerprint authentication.

MODIFICATION EXAMPLE 5

The method of presenting the evaluation result described with referenceto FIGS. 21 to 23 is merely an example, and other methods may beemployed to present the evaluation result. In addition, besides theimage, voices or the like may be used to present the evaluation result.

MODIFICATION EXAMPLE 6

It is not necessary that the texture evaluation values eval1 _(total) toeval4 _(total) be calculated by all evaluation values indicated in eachequation, but evaluation values to be used may be selected in accordancewith purpose or use. For example, in Equation (19), one or two among theevaluation value of the epidermis size distribution Eeval_(size), theevaluation value of the epidermis shape distribution Eeval_(shape), andthe evaluation value of the epidermis orientation Eeval_(direction) maybe used to calculate the texture evaluation value eval1 _(total).

In addition, for example, it is possible to remove constitutionalelements of the acquired element analysis unit 123 of FIG. 3 or theinherent element analysis unit 323 of FIG. 27 in accordance with theselection of the evaluation value used for calculating the textureevaluation value.

MODIFICATION EXAMPLE 7

The shapes of the normalization curves in FIGS. 11, 13, and 20 aremerely examples, and it is possible to use a curve that has a differentshape and indicates the same monotonic increase or monotonic decrease aseach of the normalization curves of FIGS. 11, 13, and 20.

In addition, for example, it is possible to apply the present technologyto the system or device that evaluates and diagnoses the skin state asthe beauty or health index. In addition, it is possible to apply thepresent technology to the case in which the skin state of a living bodyother than a human is evaluated and diagnosed.

[Configuration Example of Computer]

The processes described above can be executed by any of hardware orsoftware. When a series of processes is executed by software, a programconstituting the software is installed in a computer. Here, the computerincludes a computer built in dedicated hardware or, for example, ageneral-purpose personal computer capable of executing various functionsby installing various programs.

FIG. 30 is a block diagram showing an example of the hardwareconfiguration of a computer which executes the series of processes usinga program.

In the computer, a CPU (Central Processing Unit) 601, a ROM (Read OnlyMemory) 602, and a RAM (Random Access Memory) 603 are connected to eachother via a bus 604.

To the bus 604, an input/output interface 605 is also connected.Connected to the input/output interface 605 are an input unit 606, anoutput unit 607, a storage unit 608, a communication unit 609, and adrive 610.

The input unit 607 includes a keyboard, a mouse, a microphone, and thelike. The output unit 608 includes a display, a speaker, and the like.The storage unit 609 includes a hard disk, a non-volatile memory, andthe like. The communication unit 610 includes a network interface andthe like. The drive 910 drives a removable medium 611 such as a magneticdisk, an optical disc, a magneto-optical disk, or a semiconductormemory.

In the thus configured computer, for example, the CPU 601 executes aprogram stored in the storage unit 608 by loading the program in the RAM603 via the input/output interface 605 and the bus 604, and therebyperforming the above-mentioned series of processes.

The program executed by the computer (CPU 601) is provided by beingrecorded in the removable medium 611 serving as a package medium, forexample. Further, the program can be provided via a wired or wirelesstransmission medium such as a local area network, the Internet, anddigital satellite broadcasting.

In the computer, the program can be installed in the storage unit 608via the input/output interface 605, by mounting the removable medium 611on the drive 610. Further, the program can be received by thecommunication unit 609 via the wired or wireless transmission medium andcan be installed in the storage unit 608. In addition, the program canbe installed in the ROM 602 and the storage unit 608 in advance.

It should be noted that the program executed by a computer may be aprogram that is processed in time series according to the sequencedescribed in this specification or a program that is processed inparallel or at necessary timing such as upon calling.

In addition, the term system means a general device composed of aplurality of devices, means, and so forth in the present technology.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

Additionally, for example, the present technology may also be configuredas below.

(1) An image processing device, including:

an epidermis pattern detection unit configured to detect epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of skin;

an analysis unit configured to analyze uniformity of shapes of theepidermis patterns; and

an evaluation unit configured to evaluate a texture state of the skinbased on the uniformity of shapes of the epidermis patterns.

(2) The image processing device according to (1), wherein the analysisunit further analyzes at least one of uniformity of sizes of theepidermis patterns and uniformity of distributions of edge directions ofthe epidermis patterns, and the evaluation unit further evaluates thetexture state of the skin based on at least one of the uniformity ofsizes of the epidermis patterns and the uniformity of distribution ofedge directions of the epidermis patterns.

(3) The image processing device according to (1) or (2), wherein theanalysis unit further analyzes a ratio at which the epidermis patternshave predetermined shapes, and the evaluation unit further evaluates thetexture state of the skin based on the ratio at which the epidermispatterns have the predetermined shapes.

(4) The image processing device according to any one of (1) to (3),wherein the epidermis patterns are patterns formed on the epidermis byskin ridges or skin grooves.

(5) An image processing method performed by an image processing deviceconfigured to evaluate a texture state of skin, including:

detecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of the skin;

analyzing uniformity of shapes of the epidermis patterns; and

evaluating the texture state of the skin based on the uniformity ofshapes of the epidermis patterns.

(6) A program for causing a computer to execute operations including:

detecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of skin;

analyzing uniformity of shapes of the epidermis patterns; and

evaluating a texture state of the skin based on the uniformity of shapesof the epidermis patterns.

(7) An image processing device, including:

an epidermis pattern detection unit configured to detect epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of skin;

an acquired element analysis unit configured to analyze acquiredelements among elements indicating a texture state of the skin based onthe detected epidermis patterns;

an inherent element analysis unit configured to analyze inherentelements among the elements indicating the texture state of the skinbased on the detected epidermis patterns; and

an evaluation unit configured to evaluate the texture state of the skinbased on the analysis result from the acquired elements and the analysisresult from the inherent elements.

(8) The image processing device according to (7), wherein the acquiredelement analysis unit analyzes, as the acquired elements, at least oneof uniformity of shapes of the epidermis patterns, uniformity of sizesof the epidermis patterns, and uniformity of distribution of edgedirections of the epidermis patterns, and the inherent element analysisunit analyzes, as the inherent elements, the number of the epidermispatterns per unit area.

(9) The image processing device according to (7) or (8), wherein theevaluation unit calculates an evaluation value of the texture state ofthe skin by weighting and adding an evaluation value based on theanalysis result from the acquired elements and an evaluation value basedon the analysis result from the inherent elements.

(10) The image processing device according to any one of (7) to (9),wherein the epidermis patterns are patterns formed on the epidermis byskin ridges or skin grooves.

(11) An image processing method performed by an image processing deviceconfigured to evaluate a texture state of skin, including:

detecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of the skin;

analyzing acquired elements among elements indicating the texture stateof the skin based on the detected epidermis patterns;

analyzing inherent elements among the elements indicating the texturestate of the skin based on the detected epidermis patterns; and

evaluating the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

(12) A program for causing a computer to execute operations including:

detecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of skin;

analyzing acquired elements among elements indicating a texture state ofthe skin based on the detected epidermis patterns;

analyzing inherent elements among the elements indicating the texturestate of the skin based on the detected epidermis patterns; and

evaluating the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

(13) An image processing device, including:

an epidermis pattern detection unit configured to detect epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of skin;

a dermis pattern detection unit configured to detect dermis patternsthat are patterns of a dermis in a dermis image captured from the dermisof the skin;

an acquired element analysis unit configured to analyze acquiredelements among elements indicating a texture state of the skin based onthe detected epidermis patterns;

an inherent element analysis unit configured to analyze inherentelements among the elements indicating the texture state of the skinbased on the detected dermis patterns; and an evaluation unit configuredto evaluate the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

(14) The image processing device according to (13), wherein the acquiredelement analysis unit analyzes, as the acquired elements, at least oneof uniformity of shapes of the epidermis patterns, uniformity of sizesof the epidermis patterns, and uniformity of distribution of edgedirections of the epidermis patterns, and the inherent element analysisunit analyzes, as the inherent elements, at least one of uniformity ofshapes of the dermis patterns, uniformity of sizes of the dermispatterns, uniformity of distribution of edge directions of the dermispatterns, and the number of the dermis patterns per unit area.

(15) The image processing device according to (13) or (14), wherein theinherent element analysis unit further analyzes, as the inherentelement, the number of the epidermis patterns per unit area based on thedetected epidermis patterns.

(16) The image processing device according to any one of (13) to (15),wherein the evaluation unit calculates an evaluation value of thetexture state of the skin by weighting and adding an evaluation valuebased on the analysis result from the acquired elements and anevaluation value based on the analysis result from the inherentelements.

(17) The image processing device according to any one of (13) to (16),wherein the epidermis patterns are patterns formed on the epidermis byskin ridges or skin grooves, and the dermis patterns are patterns formedon the dermis by papillary layers.

(18) An image processing method performed by an image processing deviceconfigured to evaluate a texture state of skin, including:

detecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of the skin;

detecting dermis patterns that are patterns of a dermis in a dermisimage captured from the dermis of the skin;

analyzing acquired elements among elements indicating the texture stateof the skin based on the detected epidermis patterns;

analyzing inherent elements among the elements indicating the texturestate of the skin based on the detected dermis patterns; and

evaluating the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

(19) A program for causing a computer to execute operations including:

detecting epidermis patterns that are patterns of an epidermis in anepidermis image captured from the epidermis of skin;

detecting dermis patterns that are patterns of a dermis in a dermisimage captured from the dermis of the skin;

analyzing acquired elements among elements indicating a texture state ofthe skin based on the detected epidermis patterns;

analyzing inherent elements among the elements indicating the texturestate of the skin based on the detected dermis patterns; and

evaluating the texture state of the skin based on the analysis resultfrom the acquired elements and the analysis result from the inherentelements.

(20) A computer readable recording medium having recorded thereon aprogram according to (6), (12), or (19).

What is claimed is:
 1. An image processing device, comprising: aprocessing device configured to: detect epidermis patterns that arepatterns of an epidermis in an epidermis image captured from theepidermis of skin; detect dermis patterns that are patterns of a dermisin a dermis image captured from the dermis of the skin; analyze acquiredelements among elements indicating a texture state of the skin based onthe detected epidermis patterns, wherein the acquired elements analyzedinclude uniformity of sizes of the epidermis patterns; analyze inherentelements among the elements indicating the texture state of the skinbased on the detected dermis patterns; and evaluate the texture state ofthe skin based on the analysis result from the acquired elements and theanalysis result from the inherent elements.
 2. The image processingdevice according to claim 1, wherein the processing device is configuredto analyze, as the acquired elements, at least one of uniformity ofshapes of the epidermis patterns or uniformity of distribution of edgedirections of the epidermis patterns, and analyze, as the inherentelements, at least one of uniformity of shapes of the dermis patterns,uniformity of sizes of the dermis patterns, uniformity of distributionof edge directions of the dermis patterns, or the number of the dermispatterns per unit area.
 3. The image processing device according toclaim 1, wherein the processing device is configured to analyze, as theinherent element, the number of the epidermis patterns per unit areabased on the detected epidermis patterns.
 4. An image processing device,comprising: a processing device configured to: detect epidermis patternsthat are patterns of an epidermis in an epidermis image captured fromthe epidermis of skin; detect dermis patterns that are patterns of adermis in a dermis image captured from the dermis of the skin; analyzeacquired elements among elements indicating a texture state of the skinbased on the detected epidermis patterns; analyze inherent elementsamong the elements indicating the texture state of the skin based on thedetected dermis patterns; evaluate the texture state of the skin basedon the analysis result from the acquired elements and the analysisresult from the inherent elements; and calculate an evaluation value ofthe texture state of the skin by weighting and adding an evaluationvalue based on the analysis result from the acquired elements and anevaluation value based on the analysis result from the inherentelements.
 5. The image processing device according to claim 1, whereinthe epidermis patterns are patterns formed on the epidermis by skinridges or skin grooves, and the dermis patterns are patterns formed onthe dermis by papillary layers.
 6. An image processing method performedby an image processing device configured to evaluate a texture state ofskin, comprising: detecting epidermis patterns that are patterns of anepidermis in an epidermis image captured from the epidermis of the skin;detecting dermis patterns that are patterns of a dermis in a dermisimage captured from the dermis of the skin; analyzing acquired elementsamong elements indicating the texture state of the skin based on thedetected epidermis patterns, wherein the acquired elements analyzedinclude uniformity of sizes of the epidermis patterns; analyzinginherent elements among the elements indicating the texture state of theskin based on the detected dermis patterns; and evaluating the texturestate of the skin based on the analysis result from the acquiredelements and the analysis result from the inherent elements.
 7. Anon-transitory storage medium configured to store a program for causinga computer to execute operations comprising: detecting epidermispatterns that are patterns of an epidermis in an epidermis imagecaptured from the epidermis of skin; detecting dermis patterns that arepatterns of a dermis in a dermis image captured from the dermis of theskin; analyzing acquired elements among elements indicating a texturestate of the skin based on the detected epidermis patterns, wherein theacquired elements analyzed include uniformity of sizes of the epidermispatterns; analyzing inherent elements among the elements indicating thetexture state of the skin based on the detected dermis patterns; andevaluating the texture state of the skin based on the analysis resultfrom the acquired elements the analysis result from the inherentelements.